# multiply recharge by area
recharge_time_series = [i * area for i in recharge_time_series]
BFImax = 0.5
alpha = alpha_reg(discharge_time_series, area)
baseflow_time_series = filter_Eck(BFImax, discharge_time_series, alpha)
#plt.plot(discharge_time_series)
#plt.plot(baseflow_time_series)
popt, pcov, frequency_input, power_spectrum_result = discharge_ftf_fit(
recharge_time_series,
baseflow_time_series,
time_step_size,
1000,
method='scipyffthalf',
initial_guess=1e-2)
power_spectrum_input = power_spectrum(recharge_time_series,
power_spectrum_result,
time_step_size,
method="scipyffthalf",
o_i="i")[1]
plt.loglog(frequency_input, power_spectrum_result)
plt.loglog(frequency_input, power_spectrum_input)
#data = np.hstack((spectrum_input, power_spectrum_result))
#plot_spectrum(data, frequency_input, path=path, name="test")
'''
"Spectral analysis for the baseflow (not every functionality is considered (e.g. cut_index, norm))"
)
# If the current observation point is equal to the aquifer
# length, it is assumed that this polyline-file contains the
# velocities to calculate the baseflow. First, the baseflow
# is calculated and afterwards, the diffusivity is derived
# with the spectral analysis.
task_id = get_ogs_task_id(path_to_project)
baseflow = get_baseflow_from_polyline(
task_id, path_to_project,
path_to_project + "/" + task_id + "_ply_obs_01000_t" +
str(len(obs_point_list)) + "_GROUNDWATER_FLOW.tec")
# multiply the recharge time series with the aquifer length to get the total inflow
recharge = recharge_time_series * aquifer_length
try:
D, D_cov, frequency, Sqq = discharge_ftf_fit(
recharge, baseflow, time_step_size, aquifer_length)
except RuntimeError:
print("Optimal parameters not found...")
D[0], D_cov[0] = [np.nan, np.nan], [[np.nan, np.nan],
[np.nan, np.nan]]
print("popt and pcov have been set to np.nan")
except ValueError:
print(
"either ydata or xdata contain NaNs, or if incompatible options are used"
)
D[0], D_cov[0] = [np.nan, np.nan], [[np.nan, np.nan],
[np.nan, np.nan]]
except OptimizeWarning:
print(
"Covariance of the parameters could not be estimated.")
#popt, pcov = [np.nan, np.nan], [[np.nan, np.nan],[np.nan, np.nan]]
baseflow_tmp = np.loadtxt(path_to_multiple_projects + "/" + dir + "/" +
filename)
baseflow = np.column_stack((baseflow_tmp, baseflow))
baseflow_sum_1301_1400 = np.sum(baseflow, axis=1)
# load the ogs input parameter from a file
kf_list_file = open(path_to_kf_values, "r")
for i, line in enumerate(kf_list_file):
if i == 1:
splitted_line = line.strip().split(", ")
kf_geomean = float(splitted_line[0])
kf_harmean = float(splitted_line[1])
kf_arimean = float(splitted_line[2])
# calculate the power spectrum for 1001_1100: White Noise, 0.01
D_1001_1100, D_cov, frequency_1001_1100, power_spectrum_1001_1100 = discharge_ftf_fit(
recharge_1001_1100, baseflow_sum_1001_1100, 86400, 1000)
power_spectrum_1001_1100_anal = discharge_ftf(frequency_1001_1100, D_1001_1100,
aquifer_length)
power_spectrum_1001_1100_anal = np.reshape(
power_spectrum_1001_1100_anal, (len(power_spectrum_1001_1100_anal), ))
power_spectrum_1001_1100 = np.reshape(power_spectrum_1001_1100,
(len(power_spectrum_1001_1100), ))
figtxt = "OGS Input Parameter: Ss = %1.3e, D_geo = %1.3e, D_har = %1.3e, D_ari = %1.3e" % (
Ss1,
kf_geomean / Ss1,
kf_harmean / Ss1,
kf_arimean / Ss1,
) + "\nDerived Parameter: D = %1.3e, D_cov = %1.1e" % (
D_1001_1100[0],
D_cov[0],
)